Qualitative and quantitative multi-type index full-life-cycle comprehensive fuzzy evaluation method and product

Abstract

The invention relates to the technical field of engineering suitability evaluation, in particular to a qualitative and quantitative multi-type index full-life-cycle comprehensive fuzzy evaluation method and a product, and the method comprises the steps: constructing a mutual influence degree matrix in each evaluation time period, constructing a comprehensive evaluation coefficient matrix, and employing a qualitative and quantitative multi-type index comprehensive fuzzy static evaluation method. The method comprises the following steps: generating a fuzzy comprehensive evaluation value, then calculating a time period membership degree vector and an influence degree membership degree matrix, finally obtaining a comprehensive fuzzy evaluation result in a whole life cycle by weighting and summarizing the fuzzy comprehensive evaluation value of each evaluation time period, and sorting a plurality of schemes.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering suitability evaluation, and particularly relates to a comprehensive fuzzy evaluation method and product for qualitative and quantitative multi-type indicators throughout the life cycle. Background Art

[0002] Currently, in the analysis and evaluation of complex systems in multiple fields, there is a lack of systematic and standardized multi-index comprehensive evaluation methods for the comprehensive suitability evaluation of multiple scenarios or plans. Existing technologies usually evaluate single indicators, such as the stability analysis of geological conditions, the assessment of engineering construction risks, or the single calculation of resource consumption. These single-index evaluation methods are difficult to comprehensively reflect the coupling relationship and dynamic changes of multiple factors in complex systems in scenarios that require comprehensive multi-index and multi-stage characteristics.

[0003] For the comprehensive evaluation of multiple indicators, existing methods generally fall into the following categories:

[0004] Single-index evaluation methods based on monitoring data or mathematical models, which are applicable to clear index scenarios. However, these methods have limited applicability in dealing with complex scenarios with little or no monitoring data and lack the ability to analyze the mutual relationship between multiple indicators.

[0005] In the case of no data support, qualitative analysis is relied on expert experience. This method is relatively convenient in operation, but the results are easily affected by subjective factors, resulting in insufficient objectivity and scientificity of the evaluation.

[0006] In recent years, fuzzy mathematics has gradually been applied to the evaluation of multi-type indicators, and the uncertainty is characterized by introducing fuzzy membership. However, most existing methods only target static scenarios and ignore the dynamic characteristics of multiple indicators throughout the life cycle.

[0007] In addition, when existing technologies deal with multi-type indicators that are a mixture of qualitative and quantitative, it is usually assumed that each factor is independent, and the complex coupling relationship between indicators cannot be effectively captured. For example, in engineering evaluation, there are significant mutual influences between factors such as geological stability and construction cost, and existing technologies do not fully consider the overall impact of this relationship on the comprehensive evaluation results. Summary of the Invention

[0008] The technical problem to be solved by the present invention is that in the prior art, the comprehensive analysis of qualitative and quantitative multi-type indicators cannot be effectively processed in multi-index comprehensive evaluation. The purpose is to provide a comprehensive fuzzy evaluation method and product for qualitative and quantitative multi-type indicators throughout the life cycle, which realizes the dynamic comprehensive fuzzy evaluation of qualitative and quantitative multi-type indicators throughout the life cycle, reasonably introduces time period weights and influence degree matrices, and through membership calculation and weighted aggregation, comprehensively considers the complex coupling relationship of multiple indicators, and improves the objectivity, reliability and applicability of the evaluation results.

[0009] The present invention is achieved through the following technical solutions:

[0010] A qualitative and quantitative comprehensive fuzzy evaluation method for multi-type indicators throughout the life cycle, comprising:

[0011] Dividing the life cycle of the evaluation object into time periods to form multiple evaluation time periods;

[0012] Adopting a method for evaluating the mutual importance degree of fuzzy evaluation factors, constructing and normalizing the mutual influence degree matrix of influencing factors within each evaluation time period;

[0013] Constructing a coefficient matrix representing qualitative and quantitative indicators for the solutions within each evaluation time period, and normalizing the coefficient matrix;

[0014] Within each evaluation time period, based on the mutual influence degree matrix and the coefficient matrix, using a qualitative and quantitative comprehensive fuzzy static evaluation method to calculate the fuzzy comprehensive evaluation value of each solution in this evaluation time period;

[0015] Normalizing the time period weights and indicator weights of each evaluation time period, and calculating the time period membership degree vector and the influence degree membership degree matrix respectively;

[0016] Weightedly aggregating the fuzzy comprehensive evaluation values of each evaluation time period, and based on the normalized time period weight vector, obtaining the comprehensive fuzzy evaluation result of each solution within the entire life cycle range;

[0017] Sorting each solution to obtain the evaluation result.

[0018] Specifically, the method for forming multiple evaluation time periods includes:

[0019] Denoting the set of multiple solutions to be evaluated as {A i | i = 1, 2,..., m};

[0020] Recording the critical moments corresponding to each solution to form the preliminary evaluation moment set of solution A i ;

[0021] Sorting out the evaluation moment sets of all solutions and aligning the evaluation moments. If there are unaligned moments, then inserting new moments according to the state changes in adjacent time periods to obtain the supplementary evaluation moment set of solution A i ;

[0022] Combining the preliminary evaluation moment set and the supplementary evaluation moment set to obtain the complete evaluation moment set of solution A i :

[0023] Unify the set \(T\) of the complete evaluation moments of all scenarios t (A i ) and perform a unified division to generate a unified set \(\{T t |t = 1, 2, \cdots, q\}\) of evaluation periods for the entire life cycle. Each evaluation period \(T t is a time unit for fuzzy evaluation.

[0024] Specifically, the method for constructing the mutual influence degree matrix of each evaluation period includes:[[]]

[0025] Based on the direct influence degrees between every two evaluation factors, construct a self-evaluation matrix where the matrix element is the direct influence degree of the \(j\)-th factor on the \(i\)-th factor in the evaluation period \(T t , and \(n'\) is the number of evaluation factors;

[0026] Normalize the self-evaluation matrix \(X t to obtain a normalized matrix \(Y t ;

[0027] Based on the normalized matrix \(Y t construct a comprehensive influence matrix \(P t , and \(P t = Y t \cdot(I - Y t ) -1 , where \(I\) is the identity matrix;

[0028] Set the row sum of the comprehensive influence matrix \(P t as the influence degree of each evaluation factor, and set the column sum as the influenced degree of each evaluation factor;

[0029] Calculate the centrality and reason degree of each evaluation factor. Among them, the centrality is the sum of the influence degree and the influenced degree of each evaluation factor, and the reason degree is the difference between the influence degree and the influenced degree of each evaluation factor;

[0030] If there is an evaluation factor with a negative reason degree, then remove this factor and recalculate iteratively until a final influence degree matrix that meets the evaluation requirements is formed. The final influence degree matrix includes the influence degree, influenced degree, centrality, and reason degree of each evaluation factor.

[0031] Specifically, the method for constructing the coefficient matrix of each evaluation period includes:[[]]

[0032] For each evaluation period \(T t in the entire life cycle, with multiple scenarios \(\{A i |i = 1, 2, \cdots, m\}\) to be evaluated as rows, and evaluation factors \(\{Dj Taking {j = 1, 2,..., n} as columns, construct the initial coefficient matrix for this evaluation period where m is the number of solutions and n is the number of evaluation factors not excluded, is the initial assignment of the j-th evaluation factor of the i-th solution;

[0033] For the initial coefficient matrix of each evaluation period each element in is assigned. If the evaluation factor is a qualitative index, it is assigned based on the scale value given by the Delphi method, and the assignment result is normalized; if the evaluation factor is a quantitative index, it is assigned using monitoring data or calculation results, and the assignment result is normalized;

[0034] According to the different attributes of qualitative and quantitative indicators, the initial coefficient matrix is separated into multiple sub-matrices, including a qualitative sub-matrix and one or more quantitative sub-matrices;

[0035] The qualitative sub-matrix and the quantitative sub-matrix are respectively normalized. For the qualitative sub-matrix, the normalization method is used to adjust the values of all elements to the same dimension; for the quantitative sub-matrix, the normalization result is obtained by calculating according to the deviation from the target value;

[0036] The normalized qualitative sub-matrix and quantitative sub-matrix are fused to form a comprehensive evaluation coefficient matrix G t .

[0037] Specifically, the comprehensive fuzzy static evaluation method for multiple types of qualitative and quantitative indicators includes:

[0038] Based on the comprehensive evaluation coefficient matrix G t ={G ij}, determine the decision index set of each solution A i and divide it into a qualitative index set and a quantitative index set and

[0039] For the comprehensive influence degree matrix P t and the comprehensive evaluation coefficient matrix G t the decision index sets in are fuzzily assigned. For the qualitative index set trapezoidal fuzzy numbers are constructed through the Delphi method For the quantitative index set based on the monitoring data or calculation results, and combined with a reasonable target value range to construct trapezoidal fuzzy numbers

[0040] For the constructed trapezoidal fuzzy numbers and Perform defuzzification, and calculate the exact values of the evaluation indicators for each scheme A i For the qualitative index defuzzification formula, use For the quantitative index, use its normalized value as the result after defuzzification;

[0041] Obtain the comprehensive decision-making index set and calculate the maximum defuzzified value of each scheme

[0042] Calculate the fuzzy comprehensive evaluation value Ψ(A i ), When ,[[]]END]] When ,[[]]END]]

[0043] Specifically, the methods for normalizing the time period weights and index weights for each evaluation period include:

[0044] Construct the trapezoidal fuzzy number of each evaluation period T t and defuzzify the time period weights, and calculate the normalized time period membership degree vector where, is the time period weight value obtained through defuzzification processing, and k is the total number of evaluation periods;

[0045] Construct the influence degree weight parameter within each evaluation period and defuzzify the index weights, and calculate the normalized influence degree membership degree where, is the index weight value obtained through defuzzification processing, and n is the total number of evaluation indicators not excluded;

[0046] Taking the influence degree membership degree as columns and the evaluation periods as rows, construct the membership degree matrix

[0047] Specifically, the schemes for obtaining the comprehensive fuzzy evaluation results of each scheme within the full life cycle range include:

[0048] Use the index membership degree matrix to weight and integrate the indicators within each period, and calculate the comprehensive attribute evaluation value of scheme A i in the evaluation period T t where, is the weight of the j-th indicator in the evaluation period T t in the membership degree matrix, is scheme A i in the evaluation period T t ​The fuzzy comprehensive evaluation value of the j-th index.

[0049] Obtain the comprehensive fuzzy evaluation value within the full life cycle where, Ω t is the normalized time period weight of Scheme A i in the evaluation period T t Specifically, obtain the comprehensive fuzzy evaluation values corresponding to all schemes, obtain a sorted list by sorting in descending order, and obtain the optimal evaluation result.

[0050] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described above is implemented.

[0051] A computer program product includes a computer program / instructions, and when the computer program / instructions are executed by a processor, the method described above is implemented.

[0052] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0053] By constructing the mutual influence degree matrix within each evaluation period, constructing the comprehensive evaluation coefficient matrix, and using the qualitative and quantitative multi-type index comprehensive fuzzy static evaluation method, the present invention generates the fuzzy comprehensive evaluation value, then calculates the time period membership degree vector and the influence degree membership degree matrix, and finally obtains the comprehensive fuzzy evaluation result within the full life cycle by weighted aggregation of the fuzzy comprehensive evaluation values of each evaluation period, and sorts multiple schemes.

[0054] The present invention adopts the evaluation method of the mutual importance degree of fuzzy evaluation factors, which can comprehensively quantify and capture the complex coupling relationship between multiple factors, thereby enhancing the dynamic adaptability to multi-type indexes in the comprehensive evaluation process.

[0055] By the evaluation method of the mutual importance degree of fuzzy evaluation factors, the present invention comprehensively quantifies the complex coupling relationship between multiple factors, combines the separation and normalization processing of the comprehensive evaluation coefficient matrix, effectively eliminates the physical dimension differences between qualitative and quantitative indexes, and realizes the unified dynamic evaluation of multi-type indexes. Further, by constructing the mutual influence degree matrix and the fuzzy static evaluation method, the fuzzy comprehensive evaluation value of the scheme within a specific time period is accurately calculated, and at the same time, the membership degree matrix is constructed by using the normalized time period weight and the index weight, fully considering the relative importance of different evaluation stages and indexes in the full life cycle. Finally, through the weighted aggregation of the time period evaluation values, the comprehensive fuzzy evaluation result of the full life cycle is formed, which not only ensures the scientificity and objectivity of each stage evaluation, but also provides a reliable basis for the full-cycle dynamic comparison and optimization decision of multiple schemes, significantly enhancing the adaptability and decision-making support efficiency of the comprehensive evaluation system.

[0056] Brief Description of the Drawings Brief Description of the Drawings

[0057] The accompanying drawings illustrate exemplary embodiments of the present invention and, together with the description thereof, are used to explain the principles of the present invention. These drawings are included to provide a further understanding of the present invention, and the drawings are included in this specification and form a part of this specification, and do not constitute a limitation on the embodiments of the present invention.

[0058] Figure 1 It is a schematic flow chart of a qualitative and quantitative comprehensive fuzzy evaluation method for multi-type indicators in the whole life cycle according to the present invention. Detailed implementation manners

[0059] To make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and implementation manners. It can be understood that the specific implementation manners described herein are only used to explain the relevant content and do not limit the present invention.

[0060] In addition, it should be noted that for the sake of convenience of description, only the parts related to the present invention are shown in the drawings.

[0061] Without conflict, the implementation manners in the present invention and the features in the implementation manners can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and implementation manners.

[0062] Embodiment 1

[0063] As Figure 1 shown, a qualitative and quantitative comprehensive fuzzy evaluation method for multi-type indicators in the whole life cycle is provided. This method realizes the dynamic evaluation of the evaluation object within the whole life cycle through multiple evaluation steps, combining fuzzy mathematics and the theory of whole life cycle analysis, specifically including:

[0064] According to the characteristics of the evaluation object, and based on the important state change points of a specific scheme or based on preset time nodes, the whole life cycle of the evaluation object is divided into time periods to form multiple evaluation periods;

[0065] Adopt the evaluation method of the mutual importance degree of fuzzy evaluation factors to calculate the mutual influence degree between evaluation factors; construct and normalize the mutual influence degree matrix of influencing factors within each evaluation period; the mutual influence degree matrix is used to quantify the direct and indirect influence relationships between different evaluation factors to ensure that the comprehensive evaluation result is more in line with the actual situation.

[0066] Construct a coefficient matrix representing qualitative and quantitative indicators for each evaluation period, where the rows of the matrix represent different solutions and the columns represent different evaluation factors; and normalize the coefficient matrix; generate fuzzy values for qualitative indicators using the Delphi method, perform normalization on quantitative indicators based on monitoring data or calculated values, and then through separation and fusion operations, finally form a normalized comprehensive evaluation coefficient matrix.

[0067] In each evaluation period, based on the mutual influence degree matrix and the coefficient matrix, use the comprehensive fuzzy static evaluation method for qualitative and quantitative multi-type indicators to calculate the fuzzy comprehensive evaluation value of each solution in this evaluation period; the fuzzy static evaluation method can process qualitative and quantitative information simultaneously and generate an evaluation result for a specific period.

[0068] Normalize the period weights and indicator weights for each evaluation period, and calculate the period membership degree vector and the influence degree membership degree matrix respectively; where the membership degree vector and matrix reflect the relative importance of each period and each indicator in the whole life cycle evaluation.

[0069] Weight and aggregate the fuzzy comprehensive evaluation values of each evaluation period, and based on the normalized period weight vector, obtain the comprehensive fuzzy evaluation result of each solution within the whole life cycle range; reflecting the comprehensive performance of each solution during the entire evaluation cycle.

[0070] Rank each solution to obtain the evaluation result, and finally output the ranking result of advantages and disadvantages.

[0071] Embodiment 2

[0072] This embodiment provides a whole life cycle period division method, forming multiple evaluation periods, and through recording, time alignment and supplementary processing of the critical moments of the solutions, generating a period set applicable to comprehensive fuzzy evaluation, ensuring the scientificity and consistency of the evaluation results.

[0073] The method includes:

[0074] Denote the set of multiple solutions to be evaluated as {A i |i = 1, 2,..., m}, where m is the total number of solutions, and each solution A i corresponds to different evaluation objects or technical solutions.

[0075] Record the critical moments corresponding to each solution to form the preliminary evaluation time set of solution A i In this embodiment, the critical moment is generally the moment when each solution undergoes a significant change in state compared with the initial state under the action of influencing factors.

[0076] Sort out the evaluation time sets of all solutions Align the evaluation moments. The alignment operation ensures that the evaluation periods of all scenarios are consistent in terms of time nodes, which is conducive to unified comparative analysis. If there are unaligned moments, new moments are inserted according to the state changes in adjacent periods to obtain the supplementary evaluation moment set of Scenario A i of Scenario A

[0077] Combined with the preliminary evaluation moment set and the supplementary evaluation moment set to obtain the complete evaluation moment set of Scenario A i : The complete evaluation moment set is the set of all key time points in the full life cycle of the scenario, including the initially recorded moments and the supplementary inserted moments.

[0078] Unify the division of the complete evaluation moment sets T t (A i ) of all scenarios to generate a unified evaluation period set {T t |t = 1, 2,..., q} for the full life cycle. Each evaluation period T t is the time unit for fuzzy evaluation.

[0079] Example 3

[0080] The method for obtaining the comprehensive influence degree is described in detail below. The method for obtaining the comprehensive influence degree is based on an improved DEMATEL (Decision Making Trial and Evaluation Laboratory) algorithm, aiming to construct a mutual influence matrix between evaluation factors and obtain indicators such as the comprehensive influence degree matrix, centrality, reason degree, and influence degree of each factor through a series of mathematical processes. This method takes matrix operations as the core and transforms the index relationship of fuzzy evaluation into a real-number relationship. The method includes:

[0081] The evaluation factors of Scenario A i are D i , D i = (D1, D2, D3 … D n ), D i ∈R, which is a real number set. Mainly by evaluating the correlation between these factors pairwise, the factors with greater influence on the evaluation system and the factors with little influence are selected. The scale of the mutual influence degree between the indicators D i and D j can be used to construct an n-order self-evaluation matrix of index correlation

[0082] Construct a self-evaluation matrix based on the direct influence degree between each pair of evaluation factors where the matrix element is the evaluation period T​t The direct influence degree of the j-th factor on the i-th factor, where n' is the number of evaluation factors; The value of can be assigned by expert scoring or the Delphi method. For example, it can be graded according to the degree of influence: {no influence, small, medium, large, very large}, and the assigned values are {0, 1, 2, 3, 4, 5}. When i = j, When it means that index D i has no direct influence on index D j

[0083] For example, assume there is a set of 4 evaluation factors The order of the influence degrees of on the other 3 factors is (5, 3, 2), The order of the influence degrees of on the other 3 factors is (1, 4, 4), The order of the influence degrees of on the other 3 factors is (0, 0, 3), The order of the influence degrees of on the other 3 factors is (5, 1, 5). Then the constructed 4th-order index evaluation matrix

[0084] For the self-evaluation matrix X t perform normalization to obtain the normalized matrix Y t ; Normalize the self-evaluation matrix X t to obtain the normalized matrix Y t The purpose of normalization is to converge the numerical range of the matrix. For the above example matrix X t ,

[0085] Based on the normalized matrix Y t construct the comprehensive influence matrix P t , P t = Y t ·(I - Y t ) -1 , where I is the identity matrix;

[0086] Set the row sum of the comprehensive influence matrix P t as the influence degree of each evaluation factor Set the column sum as the influenced degree of each evaluation factor

[0087] Calculate the centrality and reason degree of each evaluation factor. Among them, the centrality γ i = f i + e i is the sum of the influence degree and the influenced degree of each evaluation factor, and the reason degree δ i = f​i -e i It is the difference between the influence degree and the influenced degree of each evaluation factor;

[0088] If there is an evaluation factor with a negative cause degree, then eliminate this factor and recalculate iteratively until a final influence degree matrix that meets the evaluation requirements is formed. The final influence degree matrix includes the influence degree, the influenced degree, the centrality, and the cause degree of each evaluation factor.

[0089] Example 4

[0090] In this example, taking multiple evaluation schemes as rows and evaluation factors as columns, an initial coefficient matrix is constructed. By separating the attributes of qualitative indicators and quantitative indicators and normalizing them respectively, a comprehensive evaluation coefficient matrix is finally formed. This matrix uniformly represents the comprehensive performance of the scheme during the evaluation period and depicts uncertainty and multiple types of attributes at the same time. The specific method includes:

[0091] For each evaluation period T in the whole life cycle t , taking multiple schemes {A i | i = 1, 2,..., m} to be evaluated as rows and evaluation factors {D j | j = 1, 2,..., n} as columns, construct the initial coefficient matrix of this evaluation period where m is the number of schemes and n is the number of evaluation factors that have not been eliminated, is the initial assignment of the j-th evaluation factor of the i-th scheme;

[0092] For each element in the initial coefficient matrix of each evaluation period make an assignment. If this evaluation factor is a qualitative indicator, then make an assignment based on the scale value given by the Delphi method. For example, use the score in the interval [1, 9]. The specific levels can be defined as: 1 (no influence), 3 (medium influence), 9 (great influence), and normalize the assignment result,

[0093] If this evaluation factor is a quantitative indicator, then use the monitoring data or calculation results for assignment. If there is a clear target value K ij , then normalize the assignment result, X is a reasonable value.

[0094] According to the different attributes of qualitative indicators and quantitative indicators, separate the initial coefficient matrix into multiple sub-matrices, including a qualitative sub-matrix and one or more quantitative sub-matrices;

[0095] The qualitative sub-matrix and the quantitative sub-matrix are normalized respectively. The normalization method is used to adjust the values of all elements of the qualitative sub-matrix to the same dimension; that is, all qualitative indicator values are adjusted to the same dimension according to the normalization formula.

[0096] The quantitative submatrix is normalized by using the method of deviation from the target value, that is, for each type of quantitative indicator, based on its reasonable target value K ij Normalize

[0097] The normalized qualitative sub-matrix is fused with the quantitative sub-matrix to form a comprehensive evaluation coefficient matrix G t .

[0098] Embodiment 5

[0099] This embodiment distinguishes between qualitative and quantitative indicators in the comprehensive evaluation coefficient matrix, constructs trapezoidal fuzzy numbers to describe the uncertainty and interval characteristics of the evaluation indicators, and generates a fuzzy comprehensive evaluation value for each solution through defuzzification, normalization and weighted comprehensive calculation.

[0100] First, the theoretical basis of fuzzy numbers is provided, which is a trapezoidal function. For example, fuzzy sets can be described by using excellent or large or good feasibility (δ), good or large or good feasibility (γ), medium or general or acceptable (β), poor or small or poor feasibility (α), and site suitability evaluation just meets the characteristics of trapezoidal functions.

[0101] The basic definition is as follows: Fuzzy number on the domain R The membership function Satisfying the relationship in equation 6-1, fuzzy number is a trapezoidal fuzzy number, denoted by

[0102]

[0103] Where: α≤β≤γ≤δ∈ψ,

[0104] When α=β=γ=δ, Represents a non-fuzzy number, and the exact value can also be used To express.

[0105] The G in the evaluation scheme coefficient matrix G after normalization ij The membership degree μ G :R→[0,1], for the same influencing factor X i Obviously, the solution set A i ={(X1,G1), (X2,G2),…,(X i ,Gi )} The weight coefficients satisfy the trapezoidal function model and can be scaled for the levels of poor, medium, good, and excellent using (α, β, γ, δ).

[0106] Define a method for defuzzifying a number:

[0107] Let: S be a bounded convex fuzzy number set λ (0 < λ ≤ 1), and its level cut is a real closed interval. When λ j = j / n (j = 1, 2…n), denote If R is the set of real numbers, the function D: S → R, for all have:

[0108]

[0109] And Then D is called a defuzzification function on S. From the definition of trapezoidal fuzzy numbers in the previous text,

[0110] The qualitative and quantitative multi-type index comprehensive fuzzy static evaluation method includes:

[0111] Based on the comprehensive evaluation coefficient matrix G t = {G ij}, determine the decision index set of each plan A i and divide it into a qualitative index set and a quantitative index set and

[0112] Perform fuzzy assignment on the decision index sets in the comprehensive influence degree matrix P t and the comprehensive evaluation coefficient matrix G t . For the qualitative index set Construct trapezoidal fuzzy numbers through the Delphi method

[0113] For the quantitative index set Based on the monitoring data or calculation results, and construct trapezoidal fuzzy numbers in combination with a reasonable target value range

[0114] Normalize the constructed trapezoidal fuzzy numbers and ,

[0115] Then perform defuzzification processing on the normalized trapezoidal fuzzy numbers, and calculate the exact values of each evaluation index of each plan A i . For the defuzzification formula of qualitative indexes, use Normalize the quantitative indicators to their normalized values As the result of defuzzification;

[0116] Obtain a comprehensive decision-making indicator set And calculate the maximum defuzzification value of each solution

[0117] Calculate the fuzzy comprehensive evaluation value Ψ(A i ), when hour, when hour, Right now

[0118] Embodiment 6

[0119] This embodiment describes the uncertainty of time period weights and index weights by constructing trapezoidal fuzzy numbers, converts the fuzzy numbers into specific values by using defuzzification technology, further normalizes the time period weights and index weights, and generates a time period membership vector and an influence degree membership matrix. The method includes:

[0120] Construct each evaluation period T t Trapezoidal fuzzy number is the minimum value of the trapezoidal fuzzy number, ξ t is the lower bound of the trapezoidal fuzzy number, π t is the upper bound of the trapezoidal fuzzy number, is the maximum value of the trapezoidal fuzzy number.

[0121] Defuzzify the trapezoidal fuzzy number and calculate the time period T t The weight value Then, the time period weights are defuzzified and the normalized time period membership vector is calculated. in, is the time period weight value obtained after defuzzification, k is the total number of evaluation time periods;

[0122] Construct the influence weight parameters in each evaluation period ε ij is the minimum value of the trapezoidal fuzzy number, ζ ij is the lower bound of the trapezoidal fuzzy number, η ij is the upper bound of the trapezoidal fuzzy number, θ ij is the maximum value of the trapezoidal fuzzy number.

[0123] Defuzzify the indicator weights and calculate the indicator weight values Calculate the normalized influence membership in, is the index weight value obtained after defuzzification, and n is the total number of evaluation indicators that are not eliminated;

[0124] Using the influence degree membership degree As columns and the evaluation time periods as rows, construct a membership degree matrix Each column corresponds to the membership degree of the evaluation index, and each row corresponds to the membership degree of the evaluation time period.

[0125] Example Six

[0126] In this example, by using the index membership degree matrix and the time period membership degree vector, calculate the comprehensive attribute evaluation value of each plan by time period, and perform weighted aggregation based on the full life cycle time period weight. Finally, obtain the comprehensive fuzzy evaluation value of each plan and realize the ranking and optimization of the plans. The specific method includes:

[0127] Using the index membership degree matrix Weight and integrate the indexes within each time period, and calculate the comprehensive attribute evaluation value of Plan A i In the evaluation time period T t Comprehensive attribute evaluation value Among them, Is the weight of the j-th index in the evaluation time period T of the membership degree matrix t For the j-th index, Is the fuzzy comprehensive evaluation value of the j-th index of Plan A i In the evaluation time period T t Obtain the comprehensive fuzzy evaluation value within the full life cycle range

[0128] Among them, Ω Is the normalized time period weight of Plan A t In the evaluation time period T i In the evaluation time period T t In the middle.

[0129] Obtain the comprehensive fuzzy evaluation values corresponding to all plans, and obtain a sorted list in descending order to obtain the optimal evaluation result. The plan with the largest value is the optimal plan, and the sorting result is used to support decision-making.

[0130] Example Seven

[0131] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned method is implemented.

[0132] Without loss of generality, computer-readable media can include computer storage media and communication media. Computer storage media includes volatile and non-volatile, removable and non-removable media implemented by any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer storage media includes RAM, ROM, EPROM, EEPROM, flash memory or other solid-state storage technologies, CD-ROM, DVD or other optical storage, magnetic tape cartridges, magnetic tape, disk storage or other magnetic storage devices. Of course, those skilled in the art will appreciate that computer storage media is not limited to the above several types. The above-mentioned system memory and mass storage devices can be collectively referred to as memory.

[0133] A computer program product includes a computer program / instructions that, when executed by a processor, implement the method described above.

[0134] A computer program product includes a computer program or set of instructions for performing a specific task or implementing a specific function. These programs or instructions are designed to be executable by a processor to implement a series of predefined steps or operations. The program product may be stored in various forms of computer storage media, such as memory, hard disk, solid-state drive, optical disc, or other forms of digital storage devices. It may exist in the form of compiled binary code or in the form of scripts or bytecodes executable by an interpreter. Through carefully designed algorithms and logical instructions, the program product enables the processor to process data in a specific order and manner to complete various functions such as data analysis, user interaction, device control, etc.

[0135] In the description of this specification, the description with reference to terms such as "one embodiment / way", "some embodiments / ways", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment / way or example are included in at least one embodiment / way or example of this application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment / way or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments / ways or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments / ways or examples described in this specification and the features of different embodiments / ways or examples.

[0136] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "a plurality of" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0137] Those skilled in the art should understand that the above-described embodiments are merely for clearly illustrating the present invention and are not intended to limit the scope of the present invention. For those skilled in the art, other changes or variations can be made based on the above invention, and these changes or variations are still within the scope of the present invention.

Claims

1. A comprehensive fuzzy evaluation method for the whole life cycle of qualitative and quantitative multi-type indicators, characterized in that, include: Divide the entire life cycle of the evaluation object into time periods to form multiple evaluation periods; The mutual importance evaluation method of fuzzy evaluation factors is adopted to construct and normalize the mutual influence matrix of influencing factors in each evaluation period. Constructing a coefficient matrix representing qualitative and quantitative indicators for the schemes in each evaluation period, and normalizing the coefficient matrix; In each evaluation period, based on the mutual influence matrix and coefficient matrix, the fuzzy comprehensive evaluation value of each scheme in the evaluation period is calculated by using the qualitative and quantitative multi-type index comprehensive fuzzy static evaluation method; Normalize the time period weights and indicator weights of each evaluation period, and calculate the time period membership vector and influence membership matrix respectively; The fuzzy comprehensive evaluation values of each evaluation period are weighted and summarized, and based on the normalized period weight vector, the comprehensive fuzzy evaluation results of each scheme within the entire life cycle are obtained; Sort each plan and obtain the evaluation results.

2. The comprehensive fuzzy evaluation method for the whole life cycle of qualitative and quantitative multi-type indicators according to claim 1, characterized in that Methods for forming multiple evaluation periods include: Denote the set of multiple solutions to be evaluated as {A i | i = 1, 2,..., m}, where m is the total number of solutions; Record the critical moments corresponding to each plan to form Plan A i The preliminary evaluation moment set Sort out the evaluation moment sets of all plans Align the evaluation moments. If there are unaligned moments, insert new moments according to the state changes in adjacent time periods to obtain the supplementary evaluation moment set of Plan A i The supplementary evaluation moment set Set of comprehensive preliminary evaluation times and set of supplementary evaluation times to obtain i the complete set of evaluation times for Solution A: The complete evaluation time set \(T\) of all solutions t (A i ) is uniformly divided to generate a unified evaluation time period set \(\{T t |t = 1, 2, \ldots, q\}\) for the entire life cycle. Each evaluation time period \(T t is a time unit for performing fuzzy evaluation.

3. A qualitative and quantitative comprehensive fuzzy evaluation method for multi-type indicators in the whole life cycle according to claim 1, characterized in that, The method of constructing the mutual influence matrix of each evaluation period includes: Construct a self-evaluation matrix based on the direct influence degrees between pairwise evaluation factors where the matrix element is the direct influence degree of the j-th factor on the i-th factor during the evaluation period T t , and n′ is the number of evaluation factors Normalize the self-evaluation matrix X t to obtain the normalized matrix Y t ; Based on the normalized matrix Y t Construct the comprehensive influence matrix P t , P t = Y t ·(I - Y t ) -1 , where I is the identity matrix; Set the row sum of the comprehensive influence matrix P t as the influence degree of each evaluation factor, and set the column sum as the influenced degree of each evaluation factor; Calculate the centrality and causality of each evaluation factor, where the centrality is the sum of the influence and the influence of each evaluation factor, and the causality is the difference between the influence and the influence of each evaluation factor; If an evaluation factor with a negative cause degree appears, the factor will be eliminated and the calculation will be iterated again until the final influence matrix that meets the evaluation requirements is formed, where the final influence matrix includes the influence degree, affected degree, centrality and cause degree of each evaluation factor.

4. A qualitative and quantitative comprehensive fuzzy evaluation method for multi-type indicators in the whole life cycle according to claim 1, characterized in that, Methods for constructing the coefficient matrix for each evaluation period include: For each evaluation period T in the whole life cycle t , taking multiple alternatives {A i | i = 1, 2, ..., m} as rows and evaluation factors {D j | j = 1, 2, ..., n} as columns, construct the initial coefficient matrix for this evaluation period where m is the number of alternatives and n is the number of evaluation factors that have not been eliminated, is the initial assignment of the j-th evaluation factor of the i-th alternative; For each element in the initial coefficient matrix of each evaluation period assign a value. If the evaluation factor is a qualitative index, assign a value based on the scale value given by the Delphi method and normalize the assignment result; if the evaluation factor is a quantitative index, assign a value using the monitoring data or calculation results and normalize the assignment result; ​ According to the different attributes of qualitative indicators and quantitative indicators, the initial coefficient matrix is separated into multiple sub-matrices, including a qualitative sub-matrix and one or more quantitative sub-matrices; The qualitative submatrix and the quantitative submatrix are normalized respectively. The qualitative submatrix is normalized to adjust the values of all elements to the same dimension by using the normalization method; the quantitative submatrix is normalized by using the method of deviation from the target value. Fuse the normalized qualitative sub - matrix and the quantitative sub - matrix to form a comprehensive evaluation coefficient matrix G t .

5. A qualitative and quantitative multi-type index full life cycle comprehensive fuzzy evaluation method according to claim 1, characterized in that The qualitative and quantitative multi-type index comprehensive fuzzy static evaluation method includes: Based on the comprehensive evaluation coefficient matrix G t ={G ij}, determine the decision index sets i of each solution A and divide them into a qualitative index set and a quantitative index set For the comprehensive influence degree matrix P t and the comprehensive evaluation coefficient matrix G t Fuzzy assignment is carried out for the decision-making index set in For the qualitative index set, trapezoidal fuzzy numbers are constructed by the Delphi method For the quantitative index set Based on the monitoring data or calculation results, and combined with a reasonable target value range, trapezoidal fuzzy numbers are constructed For the constructed trapezoidal fuzzy numbers and perform defuzzification, and calculate the exact values of each evaluation index of each scheme A i respectively. For the defuzzification formula of qualitative indexes, use For quantitative indexes, take its normalized value as the result after defuzzification; Obtain the comprehensive decision-making index set And calculate the maximum defuzzified value of each solution Calculate the fuzzy comprehensive evaluation value Ψ(A i ), When ), When ), 6. A qualitative and quantitative comprehensive fuzzy evaluation method for the full life cycle of multiple types of indicators according to claim 5, characterized in that, The method for normalizing the time period weight and indicator weight of each evaluation period includes: Construct each evaluation period T t of trapezoidal fuzzy numbers and defuzzify the period weights to calculate the normalized period membership vector where is the period weight value obtained through defuzzification processing, and k is the total number of evaluation periods; Construct the influence degree weight parameters for each evaluation period And defuzzify the index weights, calculate the normalized influence degree membership Among them, is the index weight value obtained through defuzzification processing, and n is the total number of evaluation indexes not excluded; Using the influence degree membership degree as columns and the evaluation time period as rows, construct a membership matrix 7. A qualitative and quantitative comprehensive fuzzy evaluation method for the full life cycle of multiple types of indicators according to claim 6, characterized in that The schemes for obtaining comprehensive fuzzy evaluation results of each scheme within the whole life cycle include: Using the index membership degree matrix Weight and integrate the indexes within each time period, and calculate Plan A i During the evaluation time period T t Comprehensive attribute evaluation value Among them, is the weight of the j-th index in the membership degree matrix during the evaluation time period T t and is the fuzzy comprehensive evaluation value of the j-th index of Plan A i during the evaluation time period T t . Obtain the comprehensive fuzzy evaluation value within the entire life cycle Among them, Ω t is the normalized time period weight of Plan A i during the evaluation period T t ​ 8. A qualitative and quantitative comprehensive fuzzy evaluation method for multi-type indicators throughout the life cycle according to claim 7, characterized in that Obtain the comprehensive fuzzy evaluation values corresponding to all schemes, and arrange them in descending order to obtain a sorted list to obtain the optimal evaluation result.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

10. A computer program product, comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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